경량화된 임베디드 시스템에서 역 원근 변환 및 머신 러닝 기반 차선 검출 (Lane Detection Based on Inverse Perspective Transformation and Machine Learning in Lightweight Embedded System)

서지정보

· 발행기관 : 대한임베디드공학회
· 수록지 정보 : 대한임베디드공학회논문지 / 17권 / 1호 / 41 ~ 49페이지
· 저자명 : 홍성훈, 박대진

초록

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird’s-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird’s-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

영어초록

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and
machine learning in lightweight embedded system. The inverse perspective transformation method is presented for
obtaining a bird’s-eye view of the scene from a perspective image to remove perspective effects. This method requires
only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom
(DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy
and speed of lane detection in complex road environments, machine learning algorithm that has passed the first
classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve
the detection speed. The first classifier is applied in the bird’s-eye view image to determine lane regions. A lane region
passed the first classifier is detected more accurately through machine learning. The system has been tested through
the driving video of the vehicle in embedded system. The experimental results show that the proposed method works
well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about
3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

참고자료

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